1
Revell AD, Wang D, Alvarez-Uria G, Streinu-Cercel A, Ene L, Wensing AMJ, Hamers RL, Morrow C, Wood R, Tempelman H, DeWolf F, Nelson M, Montaner JS, Lane HC, Larder BA on behalf of the RDI study group Abstract O234, 11th International Congress on Drug Therapy in HIV Infection, 11-15 November 2012; Glasgow Scotland
Computational models that predict response to HIV therapy may reduce virological failure and therapy costs in resource-limited settings
State of the ART
11th International Congress on Drug Therapy in HIV Infection, 11-15 November 2012; Glasgow Scotland
Key features of HIV treatment Well-resourced settings Resource-limited settings
Strategy Individualised Public health Antiretroviral drugs
- Approx. 25 from 6 classes
Limited availability / affordability Diagnostic & monitoring tools CD4, viral loads, resistance testing CD4 (Viral load?) Detection of failure Early – using regular viral loads Later – using CD4 counts or clinical symptoms Salvage Individualised – using genotype Standard protocol – genotypes unaffordable Expertise available High and multidisciplinary Mixed and thinly spread
- Rapid roll-out of ART1
- Short-term cost-savings = more patients on therapy1
- Extended the lives of millions
- Unnecessary treatment switching2
- Delayed detection of failure and deferred treatment switching3
- Increased accumulation of resistance mutations2,4
- Loss of therapeutic options5
- Increased morbidity/mortality4
Consequences of the public health strategy for resource-limited settings
1. World Health Organisation. Antiretroviral therapy for HIV infection in adolescents and adults: recommendations for a public health approach - 2010 revision. WHO; Geneva: 2010. 2. Sigaloff KCE, Hamers RL, Wallis CL, Kityo C, Siwale M, Ive P, et al. Unnecessary Antiretroviral Treatment Switches and Accumulation of HIV Resistance Mutations; Two Arguments for Viral Load Monitoring in Africa. J AcquirImmune Defic Syndr. 2011; 58(1):23-31. 3. Zhou J, Li P, Kumarasamy N, Boyd M, Chen Y, Sirisanthana T, et al. Deferred modification of antiretroviral regimen following documented treatment failure in Asia: results from the TREAT Asia HIV Observational Database (TAHOD). HIV medicine. 2010; 11(1):31-9. 4. KeiserO, Chi BH, GsponerT, Boulle A, Orrell C, Phiri S, et al. Outcomes of antiretroviral treatment in programmes with and without routine viral load monitoring in southern Africa. AIDS. 2011; 25(14):1761-1769. 5. Barth RE, Aitken SC, Tempelman H, Geelen SP, van Bussel E, Hoepelman AIM, et al. Accumulation of drug resistance and loss of therapeutic options precede commonly used criteria for treatment failure in HIV-1 subtype C infected patients. Antivir Ther 2012; 17:377-386 11th International Congress on Drug Therapy in HIV Infection, 11-15 November 2012; Glasgow Scotland
A big ?
11th International Congress on Drug Therapy in HIV Infection, 11-15 November 2012; Glasgow Scotland
What can we do to maximise the long-term effectiveness of ART in RLS? How do we get the best out of a limited range of drugs? Could the individualization of salvage ART using computational models be beneficial?
- Models can predict the response to ART with approx. 80% accuracy:
– Trained using data from many thousands of patients – Input variables including genotype, viral load, CD4 count and treatment history1,2
- Models can predict response without a genotype with a circa 70-75%
accuracy3,4
- At least comparable to the predictive accuracy of genotyping with rules
based interpretation (62-69%)5
- Models now freely available via online HIV Treatment Response
Prediction System (HIV-TRePS)
Predicting response using computational models
1. Revell AD, Wang D, Boyd MA, et al. The development of an expert system to predict virological response to HIV therapy. AIDS 2011;25:1855-1863. 2. Zazzi M, Kaiser R, Sönnerborg A, et al. Prediction of response to antiretroviral therapy by human experts and by the EuResist data-driven expert system (the EVE study). HIV Med 2010; 12(4):211-218 3. Revell AD, Wang D, Harrigan R, et al. Modelling response to HIV therapy without a genotype. J Antimicrob Chemother 2010; 65(4):605-607 4. Prosperi MCF, Rosen-Zvi M, Altman A, et al. Antiretroviral therapy optimisation without genotype resistance testing. PLoS One 2010; 5(10):e13753 5. Frentz et al. Comparison of HIV-1 Genotypic Resistance Test Interpretation Systems in Predicting Virological Outcomes Over Time. PLoS One. 2010; 5(7): e11505 11th International Congress on Drug Therapy in HIV Infection, 11-15 November 2012; Glasgow Scotland
RDI models with and without genotype and GSS from common rules systems
Larder BA et al. 49th ICAAC, 2009; H-894
Model AUC Accuracy RDI geno 0.88 82% RDI no geno 0.86 78% ANRS 0.72 66% REGA 0.68 63% Stanford db 0.71 67% Stanford ms 0.72 68%
11th International Congress on Drug Therapy in HIV Infection, 11-15 November 2012; Glasgow Scotland